Same question as my last post as raised by an audience during the academic webinar, it may sound like a common or even a silly question, but it had a deeper meaning. Because this concern is not just limited to academic professionals but also applies equally to enterprises when it comes to the security of their data.
Question: “𝐖𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐭𝐨 𝐦𝐲 𝐝𝐚𝐭𝐚 𝐨𝐧𝐜𝐞 𝐢𝐭 𝐢𝐬 𝐮𝐩𝐥𝐨𝐚𝐝𝐞𝐝, 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐭𝐡𝐚𝐭 𝐢 𝐜𝐚𝐧 𝐮𝐬𝐞 𝐥𝐨𝐜𝐚𝐥𝐥𝐲 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐞𝐱𝐩𝐨𝐬𝐢𝐧𝐠 𝐦𝐲 𝐝𝐚𝐭𝐚 𝐭𝐨 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭?”
Yes, tools like 𝐎𝐥𝐥𝐚𝐦𝐚 and others make it possible to use LLMs locally, without sending your data to the cloud.
Sounds perfect, right?
Your data stays with you. No external sharing. Full control.
𝘉𝘶𝘵 𝘩𝘦𝘳𝘦’𝘴 𝘵𝘩𝘦 𝘤𝘢𝘵𝘤𝘩… 👇
Running AI locally comes with its own set of challenges:
- Limited GPU and compute power
- Performance issues
- Scalability constraints
- And surprisingly… not the most user-friendly experience
So, the real question is:
- 𝘐𝘴 𝘭𝘰𝘤𝘢𝘭 𝘈𝘐 𝘦𝘯𝘰𝘶𝘨𝘩 𝘧𝘰𝘳 𝘳𝘦𝘢𝘭-𝘸𝘰𝘳𝘭𝘥 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦𝘴?
- 𝘖𝘳 𝘥𝘰 𝘸𝘦 𝘯𝘦𝘦𝘥 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘮𝘰𝘳𝘦 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘥 𝘢𝘯𝘥 𝘴𝘤𝘢𝘭𝘢𝘣𝘭𝘦?
- 𝘖𝘯𝘭𝘺 𝘴𝘦𝘤𝘶𝘳𝘪𝘯𝘨 𝘵𝘩𝘦 𝘓𝘓𝘔 𝘪𝘴 𝘦𝘯𝘰𝘶𝘨𝘩 𝘧𝘰𝘳 𝘶𝘴𝘪𝘯𝘨 𝘎𝘦𝘯𝘈𝘐?
- 𝘜𝘴𝘪𝘯𝘨 𝘦𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦 𝘷𝘦𝘳𝘴𝘪𝘰𝘯 𝘰𝘧 𝘗𝘶𝘣𝘭𝘪𝘤 𝘈𝘐 𝘵𝘰𝘰𝘭𝘴 𝘦𝘯𝘰𝘶𝘨𝘩 𝘵𝘰 𝘮𝘦𝘦𝘵 𝘵𝘩𝘦 𝘤𝘰𝘮𝘱𝘭𝘪𝘢𝘯𝘤𝘦 𝘯𝘦𝘦𝘥𝘴?
I’ve been exploring this thoroughly especially in context of AI, RAG, and agentic use cases, and I’ll break down all these answers in my coming posts, especially on:
- 𝐇𝐨𝐰 𝐕𝐌𝐰𝐚𝐫𝐞 𝐏𝐫𝐢𝐯𝐚𝐭𝐞 𝐀𝐈 𝐬𝐞𝐜𝐮𝐫𝐞𝐬 𝐝𝐚𝐭𝐚 𝐚𝐭 𝐞𝐚𝐜𝐡 𝐥𝐚𝐲𝐞𝐫!
Stay tuned!
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